What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Lequan Yu mainly focuses on Artificial intelligence, Segmentation, Computer vision, Convolutional neural network and Pattern recognition.
His work deals with themes such as Machine learning and Residual, which intersect with Artificial intelligence.
The Machine learning study combines topics in areas such as Gold standard and Medical imaging.
His research on Segmentation focuses in particular on Image segmentation.
His work on Medical image computing as part of general Computer vision research is frequently linked to Colonoscopy, thereby connecting diverse disciplines of science.
His study of Discriminative model is a part of Pattern recognition.
His most cited work include:
- Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks (409 citations)
- Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks (356 citations)
- VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images (349 citations)
What are the main themes of his work throughout his whole career to date?
Lequan Yu spends much of his time researching Artificial intelligence, Segmentation, Pattern recognition, Machine learning and Deep learning.
His study in the field of Image segmentation, Convolutional neural network and Artificial neural network also crosses realms of Domain.
When carried out as part of a general Segmentation research project, his work on Scale-space segmentation is frequently linked to work in Process, therefore connecting diverse disciplines of study.
In general Pattern recognition, his work in Discriminative model and Feature extraction is often linked to Fundus linking many areas of study.
His Machine learning research integrates issues from Contextual image classification and Image.
Lequan Yu works mostly in the field of Deep learning, limiting it down to concerns involving Training set and, occasionally, Test set.
He most often published in these fields:
- Artificial intelligence (92.78%)
- Segmentation (48.45%)
- Pattern recognition (43.30%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (92.78%)
- Machine learning (35.05%)
- Segmentation (48.45%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Machine learning, Segmentation, Image segmentation and Deep learning are his primary areas of study.
Artificial intelligence is closely attributed to Pattern recognition in his research.
His work carried out in the field of Machine learning brings together such families of science as Contextual image classification, Brain magnetic resonance imaging and Benchmark.
His Segmentation research is multidisciplinary, incorporating elements of Entropy, Entropy and Convolutional neural network.
As a member of one scientific family, Lequan Yu mostly works in the field of Image segmentation, focusing on Annotation and, on occasion, Margin.
His Deep learning study combines topics in areas such as Semi-supervised learning, Co-training, Robustness and Medical imaging.
Between 2019 and 2021, his most popular works were:
- MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data (30 citations)
- Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation (30 citations)
- CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of investigation include Artificial intelligence, Machine learning, Image segmentation, Deep learning and Segmentation.
The Artificial neural network, Interpolation and Metal Artifact research he does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Domain and Image restoration, therefore creating a link between diverse domains of science.
His Machine learning study integrates concerns from other disciplines, such as Contextual image classification and Image.
His research in Image segmentation focuses on subjects like Robustness, which are connected to Normalization, Magnetic resonance imaging, MS-Net and Supervised learning.
His research investigates the connection between Deep learning and topics such as Medical imaging that intersect with issues in Pattern recognition and Regularization.
His studies in Segmentation integrate themes in fields like Semi-supervised learning and Co-training.
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